Finished projects

Projects in section A which were finished at the end of the first funding period (2022):

Project A3 - Comprehensive simulation framework for modelling complex auditory discrimination experiments

The goal of this project was to provide a software tool to simulate, i.e., virtually perform, speech recognition of listeners with impaired hearing in complex communication environments. For this, a robust automatic speech recognition system was employed that does not require prior information about empirical speech recognition performance.

Based on simulations, the outcome of speech tests and also the optimal behavior (e.g., head position) for the best test result was predicted depending on the used hearing device. The focus was on understanding which (signal) properties are relevant for robust (human) speech recognition and which part of it can be integrated by listeners with impaired hearing.

Figure: Illustration of a spatial speech reception display for different hearing configurations 

Publications of A3

  • Schädler MR (2022) Interactive spatial speech recognition maps based on simulated speech recognition experiments. Acta Acoustica 6:31, 18 pages.
    DOI: 10.1051/aacus/2022028
  • Hülsmeier D, Hauth CF, Röttges S, Kranzusch P, Roßbach J, Schädler MR, Meyer BT, Warzybok A, Brand T (2021) Towards non-intrusive prediction of speech recognition thresholds in binaural conditions. 14th ITG Conference on Speech Communication, Kiel, 29 September-1 October 2021, 199-203.
    ieeexplore.ieee.org/abstract/document/9657531
  • Kramer F, Schädler MR, Hohmann V, Oetting D, Warzybok A (2020) Speech intelligibility and
    loudness perception with the trueLOUDNESS fitting rule. Proc. DAGA 2020 Hannover
  • Schädler MR (2020) Optimization and evaluation of an intelligibility-improving signal processing approach (IISPA) for the Hurricane Challenge 2.0 with FADE. Proc. Interspeech 2020, 1331-1335,
    DOI: 10.21437/Interspeech.2020-0093
  • Schädler MR, Kranzusch P, Hauth C, Warzybok A (2020) Simulating spatial speech recognition performance with anautomatic-speech-recognition-based model. Proc. DAGA 2020, 908-911
  • Siedenburg K, Schädler MR, Hülsmeier D (2019) Modeling the onset advantage in musical instrument recognition. J Acoust Soc Am 146 (6): EL523 - EL529.
    DOI: 10.1121/1.5141369
(Changed: 02 Feb 2023)